Authors: Ryan Johnson, University of Alabama
Title: The National Snow Model: Machine Learning Snow Water Equivalent Inference Model
Abstract: Snow-derived water is a critical hydrological component for characterizing the quantity of water available for domestic, recreation, agriculture, and power generation in the western United States. Advancing the efficiency and optimization of these aspects of water resources management requires an enhanced characterization of the snow state variable, particularly the essential global inputs of snow-water-equivalent (SWE), peak SWE, and snowmelt onset for hydrological models. While physically based models that characterize the feedbacks and interactions between influencing factors predict SWE values well in homogeneous settings, these models exhibit limitations for CONUS-scale deployment due to challenges attributed to spatial resolution, landscape heterogeneity, and computational intensity. Addressing these limitations, we develop the National Snow Model (NSM) as a data-driven machine learning (ML) platform with a modular structure to account for the heterogeneity of climate and topographical influences on SWE across the western United States. The NSM pipeline assimilates nearly 700 snow telemetry (SNOTEL), and California Data Exchange Center (CDEC) sites and combines with processed lidar-derived terrain features for the prediction of a 1 km x 1 km SWE inference in critical snowsheds under 20 minutes on personal computer. With preliminary regional testing performance ranging between 2.5 cm to 8 cm (i.e., RMSE), this model has the potential to advance the snow state variable in hydrological models such as the National Water Model to improve the estimates of peak flow for flood management and low-flows for supply operations.